Supporting generalized cases in conversational CBR

Abstract

Conversational Case-Based Reasoning (CCBR) provides a mixed-initiative dialog for guiding users to refine their problem descriptions incrementally through a question-answering sequence. Most CCBR approaches assume that there is at most one discrete value on each feature. While a generalized case (GC), which has been proposed and used in traditional CBR processes, has multiple values on some features. Motivated by the conversational software component retrieval application, we focus on the problem of extending CCBR to support GCs in this paper. This problem is tackled from two aspects: similarity measuring and discriminative question ranking. © Springer-Verlag Berlin Heidelberg 2005.